USING GIS AND GEOSTATISTICS TO DEVELOP ...

USING GIS AND GEOSTATISTICS TO DEVELOP HAZARD AND RISK MAPS OF ARSENIC IN SHALLOW GROUNDWATERS OF CAMBODIA

A dissertation submitted to the University of Manchester for the degree of Master of Philosophy in the Faculty of Engineering and Physical Sciences

2010

Chansopheaktra SOVANN

School of Earth, Atmospheric and Environmental Sciences

TABLE OF CONTENTS

List of figures

8

List of tables

11

Abstract

12

Declaration

13

Copyright statement

14

Acknowledgements

15

Chapter 1 Introduction

16

1.1 Introduction

16

1.2 Statement of problems

17

1.3 Rationale of the study

18

1.4 Objectives

18

1.5 Scope and limitation

19

Chapter 2 Cambodian geographic overview

20

2.1 Physical environment overviews

20

2.1.1 Geography and topography

20

2.1.2 Soil type

21

2.1.3 Land use

23

2.1.4 Climate

25

2.1.5 Geology

27

2.1.6 Hydrology

28

2.1.7 Hydrogeological structures and units

29

a. Depth of basement rocks

29

b. Hydrological units

29

c. Sedimentary profile

30

2.1.8 Groundwater resources

31

a. Specific capacity of aquifers

31

b. Groundwater level

32

c. Water balance analysis.

32 2

2.1.9 Groundwater quality in Cambodia

33

a. Bacteriological contaminant

33

b. Arsenic (As)

34

c. Iron (Fe)

36

d. Salinity

37

e. pH

38

f. Manganese (Mn)

38

2.2 Socio-economic overviews

38

2.2.1 Population

38

2.2.2 Economic

39

Chapter 3 Literature review

40

3.1 Risk assessment overviews

40

3.1.1 The Concept of risk assessment

40

3.1.2 Overview of risk assessment methods

41

3.2 Arsenic overviews

44

3.2.1 What is arsenic?

44

3.2.2 Arsenic mobilizing mechanisms in groundwater

44

3.2.3 Factors to elevate arsenic concentration to groundwater

45

3.2.4 Impact of arsenic on human health

46

3.3 Mapping of arsenic contamination in groundwater of Cambodia

47

3.4 Spatial interpolation methods

50

3.4.1 Introduction to spatial interpolation methods

50

3.4.2 Types of interpolators

50

a. Non-geostatistical interpolators

51

a.1 Linear interpolation

51

a.2 Thiessen polygon (voronoi polygon)

52

a.3 Triangular irregular network

53

a.4 Inverse distance weighting

53

a.5 Regression models

54

b. Non-geostatistical interpolators

55

b.1 Introduction of regionalized variable and kriging

55

b.2 Kriging overviews

58 3

b.3 Ordinary kriging

59

c. Combined procedures

60

c.1 Regression kriging

60

c.2 Trend surface analysis combined with kriging

61

3.4.3 Geostatistical analysis procedure

61

3.4.4 Statistical method for comparing performance of spatial interpolation methods 62 3.4.5 Some factors affecting performance of spatial interpolation methods

64

Chapter 4 Research methodology

65

4.1 Research framework

65

4.2 Study area selection

66

4.3 Secondary data acquisition

66

4.3.1 Arsenic dataset

66

4.3.2 Environmental covariates

67

a. Digital elevation model

67

b. Soil map

67

c. Geological map

67

4.3.3 Demographic database 4.4 Primary data obtained

68 68

4.4.1 Sampling site and sample size

68

4.4.2 Sampling method

69

4.4.3 Sample analysis technique

69

4.4.4 Water parameter output correction

70

4.5 Data preparation and analysis

71

4.5.1 Analysis statistic and interpreting primary data points

71

4.5.2 Arsenic data point preparation

71

4.5.3 Environmental covariates

74

a. Digital elevation model

74

b. Soil map

75

c. Geological map

75

d. Gender fraction and population density map

76

4.6 Model execution 4.6.1 Arsenic predication maps

78 78 4

4.6.2 Arsenic risk maps

79

a. Mapping of prevalence ratio and incidence rate of arsenic induced diseases

79

b. Estimations of cases of arsenic induced diseases

82

4.7 Model validations and evaluations

82

4.7.1 Arsenic prediction maps

82

4.7.2 Arsenic health risk maps

82

Chapter 5 Results

83

5.1 Primary dataset analysis result and interpretation

83

5.1.1 The quality assurance of arsenic analysis in samples

83

5.1.2 Descriptive result of arsenic concentration in samples

84

5.1.3 Correlation of arsenic with other chemical elements in samples

84

5.1.4 Piper diagrams of samples

85

5.1.5 Bicarbonate concentration in samples

86

5.1.6 Chemical quality of drinking water assessment

87

5.2 Exploring input data for model 5.2.1 Arsenic dataset

89 89

a. Training dataset transformation

90

b. Well depths attached with datasets

91

c. Elevation attached with datasets

91

d. Slope attached with datasets

92

e. Soil types attached with datasets

92

f. Geological types attached with datasets

92

g. Relations of arsenic concentration and depth of wells

93

5.2.2 Explanatory analysis focusing on the feature space

94

5.2.3 Regression modelling

95

a. Exploring qualitative relations between continuous predictors and arsenic concentration attached with training dataset

95

b. Exploring qualitative relations between discrete predictors and arsenic concentration attached with training dataset 5.3 Fitting the model

96 100

5.3.1 Variogram model for OK

100

5.3.2 Variogram model for RK

101 5

a. Principal component analysis of environmental covariates

101

b. Stepwise regression of principal components

102

c. Fitting variogram model for RK

102

5.4 Arsenic prediction mapping

103

5.4.1 Arsenic prediction map by IDW model

103

5.4.2 Arsenic prediction map by OK model

104

5.4.3 Arsenic prediction map by RK model

106

5.5 Arsenic model validation and evaluation

107

5.5.1 IDW model

107

5.5.2 OK model

109

5.5.3 RK model

109

5.6 Arsenic risk maps

113

5.6.1 Prediction maps of fraction of hyperpigmentation and keratosis

113

5.6.2 Prediction map of fraction of arsenicosis

113

5.6.3 Prediction map of fraction of skin cancer

114

5.6.4 Prediction map of incidence rate of liver cancer

115

5.6.5 Prediction map of incidence rate of lung cancer

116

5.6.6 Prediction map of incidence rate of bladder cancer

117

5.6.7 Estimation of numbers of cases of arsenic induced diseases

118

Chapter 6 Discussions

120

6.1 Piper diagrams of the groundwater samples

120

6.2 Bicarbonate contamination in samples

120

6.3 Chemical quality of drinking water assessment

120

6.4 Arsenic concentrations and depth of the wells

121

6.5 Relations between soil, geological type and arsenic concentrations

121

6.6 Arsenic prediction models

122

6.6.1 Comparison between IDW and OK models

122

6.6.2 Comparison between OK and RK models

122

6.6.3 Factors to decrease accuracy of arsenic prediction of RK model

122

6.7 Comparison of the RK model to previous study models

123

6.7.1 Comparison to Winkel et al. (2008) model

123

6.7.2 Comparison to Lado et al. (2008) model

124 6

6.8 Cross-checking results of arsenic risk maps

124

6.8.1 Cross-checking results of arsenicosis estimated by the models

124

6.8.2 Cross-checking results of arsenic induced cancers estimated by model

125

6.8.3 The uncertainty of arsenic risk maps

126

Chapter 7 Conclusions and recommendations

127

7.1 Conclusions

127

7.2 Recommendations

129

References

131

Appendix A

135

Appendix B

174

Appendix C

176

Appendix D

179

Appendix E

198

Appendix F

209

7

LIST OF FIGURES

Figure 1:

Geographical map of Cambodia

21

Figure 2:

Soil type map of Cambodia

23

Figure 3:

Landuse map of Cambodia for 2002

24

Figure 4:

The distribution of yearly average rainfall (1981-2004), yearly average temperature, and dry duration

Figure 5:

26

Cross-section of sedimentary profile in quaternary geology from the Mekong River to wetlands in Kien Svay district, Kandal province, Cambodia

Figure 6:

The distribution of different concentration of arsenic responding to geology in Cambodia

Figure 7:

31

35

Distribution of low (50 µg/L as) arsenic wells from four communes in Kean Svay district, Kandal province

Figure 8:

36

Interpolated iron concentration in groundwater contaminated along the lower Mekong River in Cambodia. The maps were drawn using

Figure 9:

a nearest neighbor algorithm, a standard geostatistical technique (n= 352)

37

Principle steps of risk assessment

42

Figure 10: The elements of risk assessment

43

Figure 11: Winkel et al.’s probability map of arsenic concentration exceeding 10 µg/L (left) and Lado et al.’s regression kriging map of arsenic concentration estimation (ppb) of 16-100 m depth groundwater (right)

49

Figure 12: The cross section of arsenic concentration variation from the wetlands to the Mekong River in Kean Svay district, Kandal province

50

Figure 13: Delauney triangulation and corresponding thiessen polygon network for a set of scatter points

52

Figure 14: The different types of variogram models: spherical model (top left), exponential model (top right), linear model (bottom left), gaussian model (bottom right)

58

Figure 15: The research framework

65

8

Figure 16: Groundwater sampling sites around Tonle Sap Lake and coastal provinces of Cambodia. Hydrological layers from MRC (2003). Based map from NIS (2008) Figure 17: Map of arsenic data point location before filtering (top) and after filtering

69 73

Figure 18: DEM after geoprocessing (left) and slope analysis in 500 m cell grid of DEM (right)

74

Figure 19: Soil map before (left) and after reclassification (right)

75

Figure 20: Geological map before (left) and after reclassification (right)

76

Figure 21: The preparation steps of arsenic dataset and environmental covariates for arsenic prediction model Figure 22: The flow diagram of the modelling of arsenic health risk maps

77 81

Figure 23: The percentage variations of different standards (top) and the percentage variations of three samples’ triplications (bottom)

83

Figure 24: The concentration of arsenic in the samples from both Tonle Sap Lake region and coastal provincial region compared to Cambodia’s and WHO standards for arsenic concentration in drinking water

84

Figure 25: The correlation matrix of all chemical elements of samples from the Tonle Sap regions (left) and coastal provincial regions (right)

85

Figure 26: The piper diagrams of samples from the Tonle Sap region (left) and the coastal region (right)

86

Figure 27: The correlation between [DIC] and [HCO3-] (left) and the comparison of [HCO3-] analysis by instrument and by titration methods in both Tonle Sap and coastal regions (right)

87

Figure 28: The stem-and-leaf plot (top) and the histogram of training dataset (bottom) 90 Figure 29: The histogram of arsenic training data after transformation

91

Figure 30: The scatter plot of arsenic concentration against depth of wells

94

Figure 31: Back to back histograms of elevation (left) and slope (right) between training samples (977 locations) and raster map (all raster nodes)

95

Figure 32: The smooth scatter plot of arsenic concentration against elevation (left) and against slope (right)

96

Figure 33: The box plot of arsenic concentration and soil types (right) and the mean plots of arsenic in different soil types with 95 % CI of mean (left)

97

9

Figure 34: The box plot of arsenic concentration and geological types (right) and the mean plots of arsenic in different geological type with 95 % CI of mean (left)

98

Figure 35: The OK variogram fitted by rule of thumb (left) and autofit method (right) 101 Figure 36: The variance of the computed principle components

102

Figure 37: The RK variogram fitted by rule of thumb (left) and autofit method (right) 103 Figure 38: The prediction map of arsenic concentration in Cambodian groundwater by the IDW model, the prediction of arsenic concentrations below 10 ppb and 50 ppb maps by the IDW model (bottom)

104

Figure 39: The prediction map of arsenic concentration in Cambodian groundwater by OK model

105

Figure 40: The prediction map of arsenic concentration in Cambodian groundwater by RK model

107

Figure 41: The plot of IDW model’s residual

108

Figure 42: The prediction variance map of OK model (top) and RK model (bottom)

111

Figure 43: The prediction maps of fractions of hyperpigmentation (left) and keratosis (right) in Cambodia

113

Figure 44: The prediction map of fraction of arsenicosis in Cambodia

114

Figure 45: The prediction map of fraction of skin cancer in Cambodia

115

Figure 46: The prediction map of incidence rate of liver cancer in Cambodia

116

Figure 47: The prediction map of incidence rate of lung cancer in Cambodia

117

Figure 48: The prediction map of incidence rate of bladder cancer in Cambodia

118

10

LIST OF TABLES

Table 1:

Soil types of Cambodia

22

Table 2:

Landuse types and their coverage in Cambodia in 2002

23

Table 3:

The average value of some climate’s variables in different months from 1961-1990 in Cambodia (at latitude 11.582° north and longitude 104.901° east)

26

Table 4:

A brief of hydro geological units in central and southern Cambodia

30

Table 5:

Distribution of groundwater arsenic by provinces

34

Table 6:

Some problems that can arise in regression analysis

54

Table 7:

Output maps capable of being produced with different kriging method in ArcGIS package

Table 8:

Table 9:

60

Description of different method to check the performance of interpolation methods

62

Parameter values for equations for arsenicosis, skin, and internal cancers

80

Table 10: Summary result of groundwater samples from Tonle Sap and coastal regions compared with who and Cambodian guidelines for drinking water

88

Table 11: The result of descriptive statistical analysis of arsenic data with the attached values of the environmental covariates

93

Table 12: The table of the results from the multi-linear regression model of log1p ([As]) as the function of elevation, slope, soil, and geological type with intercept value (top) and without intercept value (bottom) Table 13: The statistical table of cross validations of IDW, OK, RK

99 112

Table 14: The estimated cases of arsenic induced diseases for Cambodia based on Cambodian population data 2008

119

11

ABSTRACT

Arsenic is a serious hazard in groundwaters in many parts of the world, including Cambodia. In this study, Geographic Information Systems and geostatistics have been used to develop hazard and risk maps of arsenic in shallow groundwaters of Cambodia. Firstly arsenic concentration in shallow groundwaters was mapped in 500 m x 500 m sized grid by employing a regression kriging (RK) model using 1150 arsenic data points collected by various institutions between 1999 and 2010 from tube wells 16-120 m deep. The dataset was split into a training dataset (85 %) and a testing dataset (15 %). Additionally, some environmental covariates such as elevation, slope, soil types and geological types were converted to 11 principal components and then those components selected for the model by stepwise regression in order to develop the deterministic trend for the RK model. The Leave-one-out cross-validation (LOOCV) result for the RK model shows the amount of variation in arsenic concentration explained by the model is approximately 54 %, and the root of mean squared error (RMSE) is 56. The paired t-test shows that the RK model is good for predicting areas where As > 10 ppb. The results of the RK prediction maps are also compared against the IDW and OK models for evaluation. All three models predict that the high arsenic risk areas are along the Mekong River, from Kratie town to the Vietnamese border, and that Kandal province has the highest risk for arsenic in groundwater in Cambodia. The second step of the study was to generate arsenic risk maps, which was done from the RK model including ratios of arsenicosis, skin cancer, and incidence rates of arsenic induced cancers based on algorithms from Yu et al. (2003). The number of arsenic induced diseases was then estimated using the arsenic hazard map and available Cambodian population density data. The estimated number of total cases of arsenic induced diseases for the whole of Cambodia based on 2008 population statistics is 57,967 cases of arsenicosis (39,137 cases for hyperpigmentation; 18,830 for keratosis), 2,052 cases of skin cancer, and 5,690 cases of internal cancers (5,263 for liver cancer, 391 for lung cancer, and 36 for bladder cancer). The estimated numbers of arsenic-attributable deaths per annum through lung, skin and bladder cancers in 2008 were approximately 300, 0.2 and 8 cases respectively. Comparison to WHO’s estimated total deaths caused by lung, skin, and bladder cancer in 2004, shows that arsenic in groundwater contributes 28 %, 0.6 % and 6.1 % respectively to the deaths in Cambodia caused by these cancers.

12

DECLARATION

No portion of the work referred to in the dissertation has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

13

COPYRIGHT STATEMENT The following four notes on copyright and the ownership of intellectual property rights must be included as written below: 1. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. 2. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. 3. The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. 4. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://www.campus.manchester.ac.uk/medialibrary/policies/intellectual -property.pdf), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www. manchester.ac.uk/library/aboutus/regulations) and in The University’s policy on presentation of Theses.

14

ACKNOWLEDGEMENTS

I am indebted to thank EU Asia-Link CALIBRE PROJECT for their financial support for my master degree at the University of Manchester, United Kingdom. I would especially like to thank Professor David Polya for his in-depth supervision, discussion and advice during the research. His work has allowed me to gain new knowledge, particularly in geochemistry, and given me the opportunity to train and study in several leading institutions across Europe, including France, Italy and Belgium, which has greatly benefited my study. There is another set of rather special debts that I wish acknowledge is Dr. Michael LAWSON. He who has given me vital introduction of the University of Manchester especially in the School of Earth Atmospheric and Environmental Science, is the one who trained me basic lab skills and helped me to prepare and ship groundwater samples from Cambodia. A special thanks to Ms. Silke Rohn for her administrative support of the project. Thanks also to Mr. Leonid Tarasov for supporting the proof-reading stage of the write-up.

I am grateful to the following people for their lab work support and discussion: Mr. Paul Lythgoe, Mr. Alastair Bewsher, Dr. John Gaffney, Miss. Aimee Hegan, Ms. Yasmina L. de Bryant, Mr. Andrew Shantz and other Ph.D. candidates and friends.

I would also like to thank Ms. Dany Va, Mr. Sothea Kok, Professor Kim N. Irine, Mr. Chandath Him and Mr. Naro Meas for additional help and teaching during the master programme.

Special thanks to my beloved parents and to Miss. Souhour Chour for their continuous mental support.

15

CHAPTER 1 INTRODUCTION 1.1 Introduction Water dominates two thirds of the world’s surface of which oceans and fresh-water cover 97 % and 3 % respectively. Of the total fresh-water, surface fresh-water comprises merely 0.3 %, whereas icecaps and glaciers approximately 68.7 %, and groundwater 30.1 %. Usable fresh-water makes up just 1 % of the total water on Earth. The vast majority of usable freshwater is groundwater (99 %), the remainder dominated by lakes (0.86 %) and rivers (0.02 %) (Gleick, 1996).

Since water is essential to the survival of all living species, the uneven distribution of water poses serious issues for large parts of the world, leading to well-known and less-known problems like droughts and floods. At the same time, an increase in human activity and a lack of proper management of that activity have resulted in water pollution and waste, causing further shortages and illnesses. While surface water remains the primary accessible and dependable source for many communities, some seek to increase access to groundwater, particularly where clean water is in otherwise short supply. In this vein, in 2005 the United Nations launched a campaign called Decade of Water for Life (20052015). The goal of the campaign is to provide drinking water to poor communities worldwide by supporting dug wells and tube wells to extract water from aquifers (Day, 2007).

However, improper control and management of groundwater sourcing have led to overuse. In California, Texas and India, for example, the water table has lowered hundreds of feet beyond the reach of existing wells due to excessive pumping (Lall, 2009). But the issue of groundwater overuse is not the only problem; indeed, another rising concern is arsenic (As) contamination in groundwater. As contamination has been found on all major continents, including Asia, Europe, Africa, North America, and South America (Ravenscroft, Brammer, & Richards, 2009).

Long term As consumption has a direct correlation with 3 main groups of diseases: nerve and skin damage and an increased cancer risk, particularly in lungs, kidneys, liver and bladder (Hughes et al., 2009). Many people, almost 50 million in South and East Asia, 16

have been affected by long-term consumption of As contaminated water below the WHO maximum concentration limited (MCL) of 10 ppb (Ravenscroft, et al., 2009).

Cambodia is one of the areas affected by As contamination in South and East Asia. It was found to have As concentrations only after initial research in Northern India , West Bengal (1983) and Bangladesh (1993) (Ravenscroft, et al., 2009). The assessment screening results from the research conducted by the Cambodian Ministry of Rural Development and the Ministry of Industry, Mines and Energy, showed that 5 out of 13 samples of groundwater exceeded WHO As concentration limited (10 ppb). As a result of concerns over As contamination, the Cambodian government established immediate mitigation actions, including setting up the Arsenic Inter-ministerial Sub Committee, developing the Interim National Drinking Water Standard (limiting As concentration to 50 ppb, conducting testing and marking As contaminated wells, managing databases, promoting alternative water sources etc. 7 provinces of Cambodia (Kandal, Prey Veng, Kampong Cham, Kampong Chhnang, Kampong Thom, and Kratie) lying along the Mekong and Tonle Basac Rivers are identified as most at risk of As contamination. The government, as well as none government organizations’ (NGO) and many other projects, have drawn attention to the As problem by establishing strategies to protect and inform people about contaminated water.

1.2 Statement of problems Arsenic pollution has become a much-discussed issue and due to its colourless, tasteless and odourless properties it presents a difficult but interesting challenge for study. Arsenic contamination present in the sandbanks of large and tropical rivers in Asia is among the worst cases. It is predicted that human consumption of contaminated water and staple food exposed to the poison for a continuous period of 2-10 years will result in a range of diseases directly correlated with the level of arsenic intake (Ravenscroft, et al., 2009).

One among many Asian countries suffering from arsenic contamination, Cambodia’s Interim Cambodian National Drinking Water Quality Standard for arsenic contamination is set to 50 ppb , as opposed to the WHO provisional guide value of 10 ppb (D. A. Polya, et al., 2005b; Ravenscroft, et al., 2009). Though, the concentration in drinking water is reduced to 10 µg/l as the World Health Organization recommendation, potential cancer risks remain high (Tiemman, 2006; WHO., 2008).

17

The use of groundwater for consumption and irrigation is on the rise in Cambodia, especially in non-urban areas (D. A. Polya, et al., 2005b). The results of geological mapping and testing of wells published in ‘As Contamination of Groundwater in Cambodia: Strategic Action Plan 2006’ demonstrate the unevenness of arsenic contamination levels from village to village, with some wells testing for concentrations higher than 50 ppb. Although arsenic testing and research have been carried out in thousands of wells in different locations across Cambodia (MRD., 2007), there is still a general lack of data, particularly since the use of wells alters each year. Consequently, thousands of wells remain untested or present old data. Coupled with the fact that arsenic analysis is expensive, time consuming and demanding of human resources, there is a very clear gap and need for further and deeper study and analysis into arsenic contaminated groundwater. 1.3 Rationale of the study In recent years, the Cambodian government and NGO’s have increased the collection of well samples and data for various purposes. One of the principal motivations is to create a scientific and empirical basis for informed decision-making at policy level. As the result, this research study will produce an independent geospatial model for arsenic contamination in the shallow Cambodian groundwater, which can be used as a guide for scientists and policymakers alike to evaluate the necessary arsenic contamination prevention, protection and mitigation measures with a view to preempting a large-scale crisis caused by arsenic pollution. Specifically, some advantages provided by this study are: [1] a new research method of introducing geostatic and spatial data to illustrate distribution of arsenic contamination in Cambodia’s groundwater; [2] concepts for develop arsenic mitigation measure plan; [3] an effective work plan for Cambodia’s water supply.

1.4 Objectives The overall goal of this research is to prepare arsenic hazard and risk maps at a 500 m by 500 m grid scale covering the whole Cambodian territory. In order to realise the overall objective, the specific research objectives are as follows: •

to examine how the surface physical parameters of the environment explain arsenic contamination in Cambodian groundwater.

•

to map the arsenic distribution in Cambodian groundwater.

•

to produce arsenic hazard and risk maps for the whole of Cambodia. 18

1.5 Scope and limitation The current research project has a very clear scope and limitation under the research title called “Using Geographic Information Systems and Geostatistics to Develop Hazard and Risk Maps of Arsenic Contamination in Shallow Groundwater of Cambodia”. The study considers the whole of Cambodia, which covers an area of 181,035 km2 and is located at 10°-15° latitude north and 102°-108° longitude east (see chapter 2). The research is limited to shallow groundwater in the range of 16 to 120m and included data points extracted only from tube well samples. The input and output cell size of the research raster is set to a spatial resolution of 500 m by 500 m (see chapter 4).

19

CHAPTER 2 CAMBODIAN GEOGRAPHIC OVERVIEW

2.1 Physical environment overviews 2.1.1 Geography and topography Geographically, Cambodia is situated in Southeast Asia between latitudes 10º and 15º north and longitudes 102º and 108º east. It covers an area of 181,035 km2, and is bound on the west by Thailand, to the north by Laos, to the east by Vietnam, and to the south by the Gulf of Thailand.

Based on its fluctuated elevation between approximately 0 m to 1,813 m above the sea level, Cambodian topographic feature can be mainly classified into three regions: plain, highland (plateau), and mountainous regions. The plain region, sometimes known as the central plain, situates along and around the Mekong River and Tonle Sap Lake with an elevation less than 100 m above the sea level. Meanwhile, in the highland region, altitudes range from 50 up to 200 m above sea level. This region is remarkably found in some parts of provinces such as: Kompong Cham, Siem Reap, Kratie, Stung Treng, Kompong Speu, and Preah Vihear. The mountainous region is regarded as the area which has elevations approximately above 200 m up to 1,813 m. The mountainous region is conveniently subdivided into 2 zones: the northern and southwestern mountainous zones. The northern mountain zone, known in Khmer language as the Dang Reik Mountain Range, connects with the Korat Plateau of Thailand. Its altitude is in the range between 200 m and nearly 800 m (Cambodia TopoMap of 100K). The southwestern mountainous zone, broadly known as the Cardamom Mountain Range, is located in Pursat, Kampong Speu, Koh Kong, and Kampot provinces reaches heights up to 1,813 m at the highest peak in Cambodia, Aoral Mountain in Kompong Speu province (CIA, 2010; JICA, 2002). Figure of geographical map of Cambodia is given in Figure 1.

20

Figure 1: Geographical map of Cambodia

Source: redrawn from (NIS, 2008)

2.1.2 Soil type According to the Ministry of Agriculture Forestry and Fisheries and the map of Cambodian Agricultural Research and Development Institute (CARDI) after Crocker (1962), the soil type in Cambodia are classified into 16 types and four classes. The first class, high fertile soil, comprises approximately 16 % of Cambodian soils and is located along the Mekong River Plain, especially in Kampong Cham, Kandal, and Prey Veng provinces. Also, this class of soils is remarkably found in some highland zones such as some parts of Mondul Kiri, Ratanak Kiri, and Batdambang provinces. The soil types regarded as the first class of soils include alluvial lithosols, brown alluvial soils, latosols, and regurs. The second class, the medium fertile soils, constitutes about 10 % of Cambodian soils. It is found around Tonle Sap Lake and includes brown hydromorphics, and lacustrine alluvial soils. The third and fourth classes of soils cover 25 % and 39 % respectively of the total area of Cambodia. Acid lithosols, one soil type in the Fourth Class, are the most abundant soil type, covering about 25 % of the total area of Cambodia. Further details of soil type are given in Table 1 and Figure 2.

21

Table 1: Soil types of Cambodia Soil Type

Classification

Area (%)

Fourth class

25

First class

9

Alumisols

Fourth class

2

Basic lithosols Brown alluvial soils Brown hydromorphics

Fourth class

2

Agricultural potiential-remarks Optimum use: forestry. Recommended national park on west side, mountainous areas. Livestock in open forest area. Good soil: potential acidity: recommend colmatage canals. Cultivation concordance with the water regime. green manure, phosphate, and potash (avoid the use of sulphate fertilizers) Toxid soil: need organic fertilizers, rock phosphate and urea for rice. Line and drainage necessary for other crops. Optimum use: forestry, to be use for forest reserve.

First class

1

Rich soil: need phosphate and potash. Flood protection

Second class

4

Coastal complex

Third class

1

Cultural hydromorphics

Third class

8

Grey hydromorphics

Third class

10

Second class

6

Latosols

First class

4

Planosols

Third class

1

Plinthite podzols

Fourth class

10

Third class

1

Third class

14

First Class

4

Acid lithosols Alluvial lithosols

Lacustrine alluvial soils

Plinthitic hydromorphics Red-yellow podzols

Regurs

Very good rice soil: arboriculture on higher elevation and other crops. String beans: cash crop in the rice paddies. Protection against salt water. Good soil, but expensive engineering. Graded rice paddies: need organic and chemical fertilizing. Rotation with market gartering crops and beans. Better soil than cultural hydromorphics: scattered in distance areas. Difficult access planting. 80 % forests in depression and hollows. Rich Soil: need phosphate for a better yield. Possible improvement irrigation Generally good: soil needs protection from erosion and fire. Composition phosphate and organic fertilizers (Rock phosphate) Soil good: enough for rice when prepared and irrigated. Soil poorer: low agriculture potential. Covered with open forest. Reserve for extensive for livestock breeding. Cultivation not advisable. Must remain under forest. Poor: structure easily destroyed. Soil rapidly leached, lacking fertilizes elements. Rich soil: -basaltic regurs: cultivation to be encouraged -with irrigation, rice, sugar cane, pineapple, banana, seasonal crops etc. -calcic (limestone) regurs: corn, beans, banana, cotton, sugar cane

Source: (MAFF, n.d.)

22

Figure 2: Soil type map of Cambodia

Source: redrawn from (Ministry of Agriculture Forest and Fisheries, 2008; JICA Dataset, 2002)

2.1.3 Land use The overall land use in Cambodia is categorized, based on Japan Internatioal Cooporation Agency (JICA) GIS Dataset (2002) Forest (dense forest or jungle), covers much of the land in the north (Dangrek Mountain and plateau area in the Northwest) and southern part of the country (Cardamom Mountain) except area around Tonle Sap Lake and Southwest. The forest cover in 2002 was 9,140,195.83 hectares, 50.33 % of the total land area. Agriculture land use is concentrated in the Mekong delta plain and nearby Tonle Sap Lake due to the rich soils. Urban and built-up areas constitutes only 0.01 % of the total land area, mostly on lowland plians and near agricultural areas (see Table 2 and Figure 3).

Table 2: Landuse types and their coverage in Cambodia in 2002 km2

Area %

Dense Forest or Jungle

91402

50.34

Rice Field

31630

17.42

Description

23

Clear Forest

12661

6.97

Shrubland

10940

6.02

Grassland

8616

4.74

Flooded Shrub

5332

2.94

Open Water (oceans, large lakes and rivers)

4332

2.39

Field Crops

3726

2.05

Swidden Agriculture

3497

1.93

Village Garden Crops

1983

1.09

Receding Rice Fields and Floating Rice Fields

1940

1.07

Flooded Grassland

1735

0.96

Lake or Pond (Perennial)

911

0.50

Plantation

883

0.49

Mangrove

649

0.36

Marsh or Swamp

446

0.25

Barren Land

272

0.15

Flooded Forest

206

0.11

Urban, and Built-up Areas

181

0.10

Orchards

88

0.05

Sand Terrain

75

0.04

Salt Evaporator

61

0.03

Rock Outcrops

18

0.01

Source:(JICA, 2002)

Figure 3: Landuse map of Cambodia for 2002

Source: redrawn from (Save Cambodia's Wildlife, 2007; Save Cambodia's Wildlife., 2007)

24

2.1.4 Climate The country lies completely between the tropic of cancer and the equator together with some other Southeast Asian nations. The climate is monsoonal with little seasonal temperature variation (approximately 5 °C difference) but clearly distinct wet and dry seasons of relatively equal length. The southwest monsoon from the Indian Ocean brings the rainy season from mid-May to mid-September, and the northeast monsoon flow of drier, cooler air from Siberia lasts from early November to March. As a result, DecemberJanuary is the coolest period, whereas April is the warmest month of the year. The minimum temperature of 10 oC is reached in some highland regions. In contrast, in April, the maximum temperature can soar up to about 40 oC. The Atlas of Cambodia (2007) the variation of mean temperature throughout the country is less than 5 oC (MoP, 2008; MOWRAM, 2007a; Save Cambodia's Wildlife, 2007) (see Figure 4).

The total annual rainfall average in Cambodia is mostly between 1000 and 2000 mm. This amount fluctuates from year to year. In some years, notably 2000 and 2006, average rainfall dramatically increased to 1,973.5 and 1,920.4 mm respectively (MOWRAM, 2007b; Save Cambodia's Wildlife, 2007). In contrast, in 1998 average rainfall was just 1,400 mm. Rainfall varies spatially as well, particularly with topography and proximity to the coast. The heaviest rainfall occurs in the mountainous region along the coast in the southwest of the country, and which receives from 2500 to more than 5000 mm per year. In the lowland, the rainfall can be less than the highland by a factor of two or more. It is just in the range 1,000 to 1,600 mm per year. A good example can be found in Phnom Penh, located in the lowland plain, where annual rainfall was 1,326.4 mm only in 2003 (MoP, 2008; MOWRAM, 2007b; Save Cambodia's Wildlife, 2007) (see Figure 4).

25

Figure 4: The distribution of yearly average rainfall (1981-2004), yearly average temperature, and dry duration

Source: redrawn from (MoP, 2008; Save Cambodia's Wildlife, 2007)

Other climate parameters, retrieved from FAO AQUASTAT Climate Information Tool, which provide data for the global land surface at a 10 minute spatial resolution for the period 1961-1990, are summarized in Table 3.

Table 3: The average value of some climate’s variables in different months from 19611990 in Cambodia (at latitude 11.582° north and longitude 104.901° East) Month

Jan

Prc. 1

Prc.

Prc.

Wet

Tmp. 2

Tmp.

Tmp.

Grnd 3

Rel. 4

Sun

Wind 5

cv 7

days

mean

max.

min.

Frost

hum.

shine

(2m)

ETo 6

ETo

mm/m

mm/d

%

days

°C

°C

°C

days

%

%

m/s

mm/m

mm/d

7

0.2

149

1.6

26.4

31.7

21.2

0

70.8

74.4

1.8

133

4.3

1

Precipitation Temperature 3 Days of ground frost 4 Relative humidity in percentage 5 Wind speed at 2 meter above the surface in m/s 6 Reference evapotranspiration 7 Coefficient of Variation of precipitation in percentage 2

26

Feb

10

0.3

135

1.6

27.2

32.2

22.3

0

69

72.8

1.8

132

4.7

Mar

34

1.1

126

3.5

28.5

33.8

23.3

0

69

68.1

2

163

5.2

Apr

76

2.5

71.3

6.6

29.2

34.5

24

0

71.7

62.8

1.9

157

5.2

May

168

5.4

49.7

14.6

29.1

33.9

24.4

0

79.6

53

1.2

138

4.5

Jun

167

5.6

52.2

15.4

28.5

32.7

24.3

0

80.1

45.7

1.9

128

4.3

Jul

164

5.3

47.7

16.7

28

31.7

24.4

0

82

42.4

1.9

124

4

Aug

186

6

41.4

16.6

28

31.7

24.4

0

82

43

1.9

125

4

Sep

249

8.3

38

19.3

27.8

31.2

24.4

0

83.7

41

1.6

113

3.8

Oct

264

8.5

43.6

17.4

27.5

30.7

24.4

0

85.5

52.7

1.6

117

3.8

Nov

120

4

74.3

9.7

26.7

30.1

23.3

0

78.8

64.2

1.9

118

3.9

Dec

31

1

123

4.6

25.9

30.1

21.8

0

74.7

71.8

2

124

4

Total

1 477

1 573

Source: (FAO, 2010)

2.1.5 Geology According to JICA’s (2002) Geological Map Cambodia’s geology is dominated by Quaternary rocks and sediments (74 %), and Prior-Quaternary rocks (23 %). The geology of the highland and lowland areas are briefly described below. The geology of the highland area of Cambodia is dominated by the pre-Cenozoic (prior 66 million year ago) contrast to the lowland areas which mostly are covered by Cenozoic rocks and sediments.

The highland geology is dominated by: (1) Middle Jurassic-Early Cretaceous sandstones, conglomerates, and clay stones. These sedimentary rocks cover about 8.83 % of the country, particularly in the Cardamom Mountains and Northern mountain range of Cambodia, e.g. Elephant Mountains in Bokor Massif (MLMUPC, n.d.; Vysotsky, Rodnikova, & Li, 1994). (2) Middle-lower Jurassic sedimentary rocks, typically red and brown sandstones, siltstones, and marls covering about 5.34 % of the country. These rocks are found in some plateaus of Modulkiri, Ratanakkiri, StungTreng, and Preh Vihear provinces (MLMUPC, n.d.; Vysotsky, et al., 1994). (3) Post Triassic and Triassic rocks, notably granite, sandstone mixed with microbreccia, breccias, conglomerate mixed with breccia, and also some formation of siltstone with schists and marl too. They are dominated 3.51 % of the land. The total thickness of the upper part of these rocks is estimated to be 1,000-2,000 m. 27

These rocks are found mostly in Mondulkiri’s plateaus, Aoral Mountain, and as well Pailin province l (MLMUPC, n.d.; Vysotsky, et al., 1994). (4) Pre Paleozoic-Paleozoic rocks, covering 2.8 % of Cambodia comprising three main rock types: igneous rocks (acid tuff, andesite, old rhyolite, etc.), metamorphic rocks (crystallized amphibolite, phtanite, quartzite, schist and sandstone, etc.), and sedimentary rocks (limestone and crystallized limestone) (MLMUPC, n.d.; Vysotsky, et al., 1994). (5) Pre Quaternary basalt, covering about 0.5 % of Cambodia notably in the eastern plateau of Cambodia especially in Ratanakiri and Mondokiri province (MLMUPC, n.d.; Vysotsky, et al., 1994).

The geology of the lowland areas of Cambodia is almost completely dominated by Cenozoic sediments and lesser volcanics. Their geological features are also strongly controlled by the Mekong River system. It is estimated that Mekong River discharges approximately 160 million tons of sediments and 30 % of the world’s dissolved salts contributed to oceans per year. Through the huge amount of sediment supply with the very low slope of Cambodia’s topography especially at the central, southwest, and along Mekong River, some 10,000 km2 of the land are stretched by the Mekong delta floodplain. (Meybeck & Carbonnel, 1975). The present Mekong delta was formed between 600 and 10,000 years ago (i.e. during the Holocene or Recent epoch) and is found in forms of Floodplains, organic deposits (swamps), Alluvial Plain Deposits, Alluvial fans, amongst others (MLMUPC, n.d.; Tamura, et al., 2007).

2.1.6 Hydrology Cambodia can be regarded as one the countries most enriched in water resources. On the surface, the Mekong River flows across Cambodia from the north to the southeast over a distance of 480 km. The total catchment covers 86 % of the country and is able to store 475,000 million m3 of the average annual floodwaters. Moreover, the maximum monthly water flow can increase up to 38,719 m3/sec in rainy season, as indicated by the records of Kampong Cham Station in September (MRC, 2003; MRC & UNEP, 1997; Pen & Pin, N.A.).

Flowing on the average topographical slope of less than 10° from the border of Cambodia and Lao PRD to the center of Stueng Treng province in a distance of about 50 km, the 28

Mekong River is joined by the Sesan River which is a major river combined by other three rivers including Sekong, San River, and Srey Pok Rivers. These tributaries play a significant role in gathering rainwater from the Northeast plateau of the country and some of the highlands of Vietnam. At Phnom Penh junction, Mekong River subdivides into three. The main river is called “Lower Mekong River” and the subsequent one is called “Bassac River”. Both flow downstream to southern Vietnam’s delta. The last one is the “Tonle Sap River” which connects the Mekong River to the Great Lake. Surprisingly, Tonle Sap River is the largest natural river which has reversal of flows. This means that in the flooded season water flows from Mekong River to the Great Lake and reverses flow in the dry season (MRC, 2003; MRC & UNEP, 1997).

2.1.7 Hydrogeological structures and units a. Depth of basement rocks Results from a geophysical survey and well drilling tests carried out by JICA in the southern and central parts of Cambodia from December 1996 to March 2002 (drill 30 wells in the center, and 26 in the South) reveal that the depth of the basement rocks in the studied area vary from just 1 m to more than several hundred meters from the surface. In Kampong Speu province, the basement rocks are found at very shallow depths between 1 to 30 m. However, in the mountain valley, the basement rocks are found at depths from 60 to 84 m. In Phnom Penh, the basement rocks also lie at shallow depths of approximately 9 to 76 meters; whereas basement rocks in the Southern Kandal province, situated along the Basac River and the eastern bank of Mekong River, are deeper than 160 m. In Takeo and Kampong Chhnang provinces, the depth of the basement rocks ranges from 12 to 36 m and from 10 to 30 m respectively. Additionally, the basement rocks are also found as deep as several hundred meters in some parts of the Kampong Cham province. to 70 meters (JICA & MRD, 2002a, 2002b).

b. Hydrological units In Cambodia, they are several types of aquifers, aquitards, and aquicludes. In central and southern Cambodia, there are just a few major hydrogeologic units, including Holocene alluvial deposits and some Pleistocene deposits which are widespread in the lowland plains. Further details on hydrological units are presented in Table 4.

29

Table 4: A Brief of hydro geological Units in central and southern Cambodia Geological Age Era

Holocene

Pleistocene

Pliocene

Pre-Tertiary

Hydrogeologic Unit

Alluvial deposits

Mainly aquitard/Mainly

Old river deposits

10,000-nowadays

2 millions-10,000

5-2 millions

more than 66 millions

Mesozoic

Mainly aquitard/Mainly aquiclude Mainly aquiclude

Flood plain deposits

Aquifer/Aquiclude

Plateau deposits

Aquifer/Aquitard

Plateau Basalt

Aquifer

Fissure zone Flowing

Terrace-Plateau deposits

Aquifer/Aquitard

Terrace & platform deposits

Aquifer/Aquitard

Higher platform deposits

Aquifer/Aquitard

Terrace-Plateau deposits

Aquifer

Plateau Basalt

Aquifer

sand stone rhyolite granite Volcano-sedimentary units

Pre-Cambrian to

Remark

Alluvial valley deposits Quaternary

Tertiary

Geology Year

more than 538-66 millions

Aquifer

Weathered zone and Fissure

Impermeable basement

Fresh compact zone

Aquifer

Granitic rocks Sedimentary rocks

Old Alluvium

Aquitard

Weathered zone and Fissure zone Fresh rock

Metamorphic rocks Source: (Graham R. Thompson & Turk, 1997; JICA & MRD, 2002a, 2002b)

c. Sedimentary profile According to drill core and drill cutting data of JICA et al. (2002b), Kocar et al. (2008), and Polizzotto et al. (2008), there are three main groups of sediments above bedrock. The first main layer is top soil. Its depth can be deeper than 2.5 m from the ground surface, e.g Khvet village, Phnom Penh (JICA & MRD, 2002b). Then, the second major layer frequently is clay layers, particularly brown clay and grey clay. These clay layers can be found up to 20 m deep (JICA & MRD, 2002b; Kocar, et al., 2008; Polizzotto, Kocar, Benner, Sampson, & Fendorf, 2008). The third main layer is regular sand layers, a good aquifer for groundwater resources. These layers can extend to greater than 120 m depth from the ground, e.g. in Koy Tra Bek Village, Svay Rieng province (JICA & MRD, 2002b). The sand layers in the quaternary area range in thickness up to 120 m (JICA & MRD, 2002b). For specific example of sedmentary profile is in Figure 5.

30

Figure 5: Cross-section of sedimentary profile in quaternary geology from the Mekong River to wetlands in Kien Svay district, Kandal province, Cambodia

Note:

TC51

X=501250 Y=1271887;

TH71

X=500938 Y=1273415

TE61

X=501766 Y=1273456;

TE11

X=501872 Y=1274279

Source: (Polizzotto, et al., 2008)

2.1.8 Groundwater resources a. Specific capacity of aquifers The specific capacity of aquifers in Cambodia varies from less than 1 m3/day up to 670 m3/day. This divergence is caused by two main reasons: the different aquifer types and the topography. For the types of aquifers, it has been shown that Plio-Pleistonce aquifers, a type of quaternary aquifer, are the best aquifers in terms of providing the highest quantity of groundwater. Some parts of Kampong Cham and Kampong Chhnang provinces contain this kind of aquifer and the groundwater yield can range from 10 m3/day to a maximum of 670 m3/day. In January 1996, it was recorded that JICA has uncovered the most productive well around Phnom Penh which could generate up to 200 L/min. There are seven highly productive wells which supply safe and clean water around Phnom Penh: these wells generate water at about 80 L/min at the depth of 18 to 80 meter in the Holocene alluvial deposit aquifers. Meanwhile, other quaternary aquifers in lowland areas such Svay Reing, Prey Veng, and Kandal provinces also produce a satisfactory amount of groundwater. Their specific capacities vary between 25 m3/day and 150 m3/day. In contrast, basaltic aquifers do not provide large amounts of groundwater like quaternary aquifers. Specifically, in the eastern Kampong Cham province, the basaltic aquifers only supply approximately 5.5 m3/day to 27.9 m3/day of groundwater. The influence of the topography 31

on the specific capacity of groundwater has also been proved. The same type of aquifers in a different topography can result in a different groundwater capacity. Particularly, the specific capacity of basement rock aquifers in the lowland of Memot district, Kampong Cham province, is much higher than that in the highland of Kampong Speu province. The higher yields managed between 6.7 m3/day to 114.6 m3/day and 0.5 m3/day to 10 m3/day for each location respectively (JICA & MRD, 2002a, 2002b; Pen & Pin, N.A.).

b. Groundwater level Cambodia’s groundwater yield is higher at the central and eastern lowlands than in the Western highland areas. This can be caused by many factors contributing to the change of groundwater level. However, among all of the factors, geography and rainfall level are the two major causes. In some provinces of Cambodia situated near the Mekong River and Tonle Sap River, the groundwater levels changed remarkably according to river levels. The groundwater level rises sharply during June and July which mirrors the sharp increase of the river water level at that time. As a result, the groundwater level’s amplitude seems higher than in the case of other areas located far away from river sites. For example, the amplitude of the groundwater level in Kandal province especially in the center is up to 7 m. Measuring from the ground surface, it changes from a low level at 0.5 m to its deepest level at 7.5 m. Another good example is the groundwater level from Prey Veng; as it canges from 1 m to 6 m. In contrast, in the Kampong Speu province, which is situated far away from the main rivers, the amplitude is only approximately 4 m. Rainfall previously reported also influences groundwater level changes. Most of the groundwater in Cambodia reaches their shallowest level during September until November, and their deepest level during the period from April to July. The different rainfall levels registered in different months can cause this. In central and southern Kampong Chnnang, the groundwater level changes from 3 m in February to 7 m in May. In addition, in the central Kandal province, it also changes from over 3.5 m depth in February to 5 m depth in March.

c. Water balance analysis. To estimate the amount of groundwater recharging from the rainfall in southern and central Cambodia, a water balance modeling tool called SCS model, has been used. It is simulated by using some variables such as: (1) land use and geomorphologic conditions, (2) daily rainfall input data from 1986 to1995, (3) monthly average pan-evaporation, (4) soil types and other parameters. In central of Cambodia, Kampong Cham and Kampong Chhnang 32

province, the estimated result for the water balance is unfolded in the annual average rainfall of 1,316 mm; the annual average groundwater is recharged by an amount of 448 mm (48 %). The maximum and minimum recharged volumes are equal to 137 mm in September and 0 mm from December to February respectively. Furthermore, the monthly average groundwater recharge volumes for other months vary depending on the amount of rainfall, and are: 10 % from March to April, 35 % in May, 20-30 % from June to August, and 42-47 % from September to November. Other variables related to the of water balance are also simulated under the same conditions; they are approximately 38.8 % for actual transpiration, 17.6 % for actual evaporation, 7.7 % for surface runoff, and 1.7 % for plant undertake (JICA & MRD, 2002a, 2002b). Besides southern Cambodia, using the same input rainfall data from 1986-1995, the simulated result for the annual average groundwater recharge amount is 448.3 mm (34.1 %). Still, the maximum and minimum annual average groundwater recharge volume is 649.6 mm in 1985 and 315.4 mm in 1992 respectively. The annual average of actual evaporation is estimated to be 510.9 mm (38.8 %), while the annual average of surface runoff is about 102 mm (7.75 %) (JICA & MRD, 2002a, 2002b). 2.1.9 Groundwater quality in Cambodia Even if Cambodia is a country rich in groundwater resources, some of the groundwater resources are not safe enough for direct supply for drinking purpose. It is strongly recommended that water is treated or boiled before use since some well waters are contaminated by biological and chemical pollutants which exceed the WHO's Guidelines for Drinking Water Quality (WHO's GDWQ) and the Cambodian Drinking Water Quality Standards (CDWQS). For example, in the JICA report of groundwater development in central and southern Cambodia, it is stated that in Kompong Cham and Kompong Chhnang province, only 29 % of 31 dug well samples and 24 % of 34 tube well samples completely satisfy the WHO’s GDWQ. Meanwhile, almost all of the 284 sampled wells from southern Cambodia province did not comply with WHO’s GDWQ (JICA & MRD, 2002a, 2002b). Below are some related groundwater parameters which have been reported in Cambodia’s groundwater resources. a. Bacteriological contaminant These contaminants are the ones of most concern for Cambodia’s groundwater quality. They can be found in many open dug wells which extract the water from shallow aquifers 33

(approximately less than 20 m in depth). Based on the sampling of 414 open wells and 223 tube wells in the Kandal province by RDI-C, the results show proves that 77 % the samples from open wells and 43 % of those from the tube wells exceed 0 cfu/100 ml CDWQS. Furthermore, the mean value for E. Coli in the open well samples are two times higher than that from the tube wells, which are 1325 and 597 cfu/100 ml respectively. According to represents this statistic, open dug wells are more likely to be contaminated by pathogens, since E. Coli is potential faecal contamination indicates the presence of pathogens (Bennett, Shantz, Shin, Sampson, & Meschke, 2010; Feldman, et al., 2007; M. Sampson, 2008). b. Arsenic (As) In December 2009, it was estimated that approximately 1,607 villages in more than seven provinces in Cambodia such as Kandal, Prey Veng, Kampong Cham, Kampong Chhnang, Kampong Thom and peri-urban Phnom Penh had high arsenic concentrations in the groundwater. Amongst these, Kandal province is reported as at highest risk since it has the highest statistic of well waters exceeding 50 ppb of As CDWQS. Of 15143 tested wells, 35 % had a concentration of arsenic exceeding 50 ppb. This number is followed by Kompong Cham province, which has 33 % of 7,455 tested wells (MRD., 2007; Phan, 2009; M. L. Sampson, Bostick, Chiew, Hagan, & Shantz, 2008). The As concentration in the well waters can be as high as 3,400 ppb, recorded in Preak Russey Commune, Kien Svay district, Kandal province (M. L. Sampson, et al., 2008). Other provinces such as Kratie, Preah Vihear, and Pailin also report very high values. However, the situation is not as serious as the case in the southern provinces (D. A. Polya, et al., 2005a). Futher details of distribution of arsenic in groundwater by provinces are given in Table 5. Table 5: Distribution of groundwater arsenic by provinces Province

Well Tested

As>50

%

As=0-50

%

As=0

%

Kandal

15143

5325

35

5789

38

4029

27

Kampong Cham

7455

2477

33

4318

59

660

8

Prey Veng

5641

1055

19

1887

33

2699

48

Kampong Thom

1346

40

3

799

59

507

38

Kratie

734

110

15

199

27

417

58

Kampong Chhnang

294

19

6

131

44

144

50

Peri-urban Phnom Penh

226

22

10

91

40

113

50

Source: (Phan, 2009)

34

Elevated arsenic Contamination in groundwater is likely to have a direct correlation with the local geology. It has been observed that the Mekong and Bassac River floodplains, which are covered by Holocene alluvial plains, have highest number of sampled wells (40 %) with arsenic concentrations exceeding the CDWQS. In other areas covered by Holocene and Quaternary sediments, the percentage of sampled wells with arsenic levels above the recommended value drops to 10 %. Lastly, in wells sampled in other types of areas such as Pliocene Volcanics and Neogene-Quaternary sediments, as well as other groups of older units, the percentage of highly contaminated wells falls further to 5 % (D. A. Polya, et al., 2005a; David A. Polya, et al., 2010) (see Figure 6). Figure 6: The distribution of different concentration of arsenic responding to geology in Cambodia

Source: (David A. Polya, et al., 2010)

Different types of wells and different depths in the high risk arsenic regions also show a correlation with to high concentration of arsenic in groundwater as well. Most of the arsenic concentration which exceeds the WHO standards of 10 ppb can be found in the shallow wells, especially in the range between 16 m and 70 m deep. Meanwhile, the wells in depths of over 70 m are still understudied, in part because of the shallow depth of basement in many areas. However, the case in Bangladesh and West Bengal, India, reveal that over the depths of 70 m, wells are likely to have a trend of low As release (Buschmann, et al., 2008; Feldman, et al., 2007; Kocar, et al., 2008; Polizzotto, et al., 2008; D. A. Polya, et al., 2005a; David A. Polya, et al., 2010; M. L. Sampson, et al., 2008). Level of arsenic release to well waters is likely to be influenced by the well types. Tube wells are likely to have a trend of higher arsenic concentration than open wells. For 35

example, a recent study in Kandal province by Research Development Institute (RDI) has proved that the percentage of the tube well samples over CDWQS are approximately four and a half times higher than in the open well samples. The percentages of sample with high arsenic are 32 % out of 223 tube well samples and 7 % out of 414 open well samples respectively (M. Sampson, 2008) (see Figure 7).

Figure 7: Distribution of low (50 µg/L As) arsenic wells from four communes in Kean Svay district, Kandal province

Source: (M. L. Sampson, et al., 2008)

c. Iron (Fe) There is no specific location for the occurrence of high iron concentration in Cambodia’s groundwater. It is likely that cases of high concentration of iron in the groundwater can take place everywhere in Cambodian groundwater. However, it is estimated that iron concentration at some floodplain regions along the Mekong River from the center of Kampong Cham province to the Vietnamese border exceeds 300 ppb of CDWQS. Additionally, the highest iron concentration in Cambodian well waters can be as high as 13,600 ppb, which this case happened in the Veal Sbov Village, Veal Sbov Commun, Kien Svay district, Kandal province (Buschmann, et al., 2008; Feldman, et al., 2007) (see Figure 8).

36

Figure 8: Interpolated iron concentration in groundwater contaminated along the Lower Mekong River in Cambodia. The maps were drawn using a nearest neighbor algorithm, a standard geostatistical technique (n= 352)

Source: (Buschmann, et al., 2008)

d. Salinity Once the concentration of salinity exceeds 1 g/L, the taste of groundwater makes it unsuitable for drinking. In Cambodian groundwater, the level of salinity can be as high as 10 g/L, as has been found in Chantrea and Kampong Rou district, Svay Rieng province, as well as in Kampong Trach district, Kompot Pronvice. Some parts of provinces experiencing high levels of salinity include: Koh Kong, Shihanou Ville, Kampot, Kep, and Svay Rieng province. All of these provinces, excluding Svay Rieng province, share the borders with the sea (Buschmann, et al., 2008; MIME, 2004; WHO, 2008). 37

e. pH Within the CDWQS pH is regarded as one of the priority parameters especially for the determination of standard the water quality for small water supply facilities. For the purposes of safe drinking water, the level of pH is set in the range of 6.5-8.5. In Cambodia, several well waters do not fulfill this standard, most of them being below pH 6.5 rather than over 8.5. According to the assessment of the chemical quality of drinking water by WHO in 2001, there were 26 of 94 groundwater samples which did not comply with this standard.

Over 90 % of them were lower than pH of 6.5. They are spread out in

northeastern Prey Veng and Svay Rieng, western Takeo, and some areas around the Great Lake. However, the pH of alkaline well waters is prone to fluctuations. For instance, the pH of alkaline groundwater in center and west of Kompong Cham changed from higher than 7 in February to be lower than 7 in May of the same year, 2001 (Feldman, et al., 2007; JICA & MRD, 2002a; MIME, 2004).

f. Manganese (Mn) Manganese is another parameter of concern with regards to safe drinking water. According to the assessment of the chemical quality from drinking water by WHO in 2001, 22 % of 94 well samples exceeded WHO the standard of 400 ppb; whereas it was reduced to 17 % once the 500 ppb CDWQS was applied. The maximum observed was 4,080 ppb in Kampong Pring Commune, Bat Dambang province. In addition to that, some regions along the Mekong River also presented high contamination by manganese in the groundwater, especially south of Phnom Penh. Towards the south of Phnom Penh, (including the Mekong River from Phnom Penh to Vietnam) the mean concentration of manganese in well waters is likely higher than north of Phnom Penh, where it included the area along the Mekong River from the center of the Kompong Cham province to Phnom Penh. The value are 400 ppb (ranging from 10) length(as.rk.default.as.10) stat.desc(as.rk.default.as.10)

windows() hist(log10(as.rk.default.na.omit), breaks=50, col="white", main="Histogram of [As] predicted by R-K with rule of thumb variogram", xlab="Log10 [As] (ppb)") abline(v=1.69897, lty=2, col="red") # line of arsenic 50 text(1.85, 80000, "50 ppb", col="red") abline(v=1, lty=2, col="green") text(1.15, 10000, "10 ppb", col="green")

# RK by automatic variogram step.as$call$formula # Calls the formula of regression model as.rk = krige(step.as$call$formula, dataset.ov, pc.comps, as.rvgm$var_model) as.rk$var1.rk = expm1(as.rk$var1.pred) # create column of Back-transform the values as.rk$var1.rk0 = replace(as.rk$var1.rk,as.rk$var1.rk50) length(as.rk.as.50) stat.desc(as.rk.as.50)

as.rk.as.10 = subset(as.rk.na.omit, subset=as.rk.na.omit>10) length(as.rk.as.10) stat.desc(as.rk.as.10)

windows() hist(log10(as.rk.na.omit), breaks=50, col="white", main="Histogram of [As] predicted by R-K with autofit variogram", xlab="Log10 [As] (ppb)") abline(v=1.69897, lty=2, col="red") # line of arsenic 50 text(1.85, 80000, "50 ppb", col="red") abline(v=1, lty=2, col="green") text(1.15, 10000, "10 ppb", col="green")

# Write RK model to ascii file write.asciigrid(as.rk["var1.rk"], "as_rk.asc", na.value=-1)

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write.asciigrid(as.rk.default["var1.rk"], "as_rk_default.asc", na.value=-1) write.asciigrid(as.rk["var1.rk0"], "as_rk_no_neg.asc", na.value=-1) write.asciigrid(as.rk.default["var1.rk0"], "as_rk_default_no_neg.asc", na.value=-1)

########### # Ordinary-Kriging (OK) # OK by rule of thumb variogram as.ok.default = krige(log1p(as) ~ 1, dataset, dataset.grid["soil"], as.vgm.default) as.ok.default$var1.rk = expm1(as.ok.default$var1.pred) as.ok.default$var1.rk0 = replace(as.ok.default$var1.rk,as.ok.default$var1.rk50) length(as.ok.default.as.50) stat.desc(as.ok.default.as.50)

as.ok.default.as.10 = subset(as.ok.default.na.omit, subset=as.ok.default.na.omit>10) length(as.ok.default.as.10) stat.desc(as.ok.default.as.10)

windows() hist(log10(as.ok.default.na.omit), breaks=50, col="white", main="Histogram of [As] predicted by OK with rule of thumb variogram", xlab="Log10 [As] (ppb)") abline(v=1.69897, lty=2, col="red") # line of arsenic 50 text(1.85, 250000, "50 ppb", col="red") abline(v=1, lty=2, col="green") text(1.15, 150000, "10 ppb", col="green")

# OK by autofit variogram as.ok = krige(log1p(as) ~ 1, dataset, dataset.grid["soil"], as.vgm$var_model) as.ok$var1.rk = expm1(as.ok$var1.pred) as.ok$var1.rk0 = replace(as.ok$var1.rk,as.ok$var1.rk50) length(as.ok.as.50) stat.desc(as.ok.as.50)

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as.ok.as.10 = subset(as.ok.na.omit, subset=as.ok.na.omit>10) length(as.ok.as.10) stat.desc(as.ok.as.10)

windows() hist(log10(as.ok.na.omit), breaks=50, col="white", main="Histogram of [As] predicted by OK with autofit variogram", xlab="Log10 [As] (ppb)") abline(v=1.69897, lty=2, col="red") # line of arsenic 50 text(1.85, 250000, "50 ppb", col="red") abline(v=1, lty=2, col="green") text(1.15, 150000, "10 ppb", col="green")

# write OK model to ascii write.asciigrid(as.ok["var1.rk"], "as_ok.asc", na.value=-1) # the right value of prediction write.asciigrid(as.ok.default["var1.rk"], "as_ok_default.asc", na.value=-1) write.asciigrid(as.ok["var1.rk0"], "as_ok_no_neg.asc", na.value=-1) # the right value of prediction write.asciigrid(as.ok.default["var1.rk0"], "as_ok_default_no_neg.asc", na.value=-1)

########### # Universal-Kriging (UK) #Uk by default variogram as.uk.default = krige(log1p(as) ~ x+y, dataset, dataset.grid["soil"], as.vgm.default) as.uk.default$var1.rk = expm1(as.uk.default$var1.pred)

#Uk by manual variogram as.uk = krige(log1p(as) ~ x+y, dataset, dataset.grid["soil"], as.vgm$var_model) as.uk$var1.rk = expm1(as.uk$var1.pred)

# Write UK model to ascii file write.asciigrid(as.uk["var1.rk"], "as_uk.asc", na.value=-1) write.asciigrid(as.uk.default["var1.rk"], "as_uk_default.asc", na.value=-1)

########### # Krige with External Drift (KED) #as.ked = krige(log1p(as) ~ dataset@data$soil_v7, dataset, dataset.grid["soil"], as.vgm) #as.ked$var1.rk = expm1(as.ked$var1.pred)

########### #Inverst Distance Weighting (IDW) as.idw = krige ( as ~ 1, dataset, dataset.grid["soil"])# the same function as

as.idw = idw ( as ~ 1, dataset, dataset.grid["soil"], idp=2)

as.idw.na.omit = na.omit(as.idw$var1.pred) desc.stat.as.idw = stat.desc(as.idw.na.omit) #Descripttive statsitic length(as.idw$var1.pred) length(as.idw.na.omit) desc.stat.as.idw

as.idw.as.50 = subset(as.idw.na.omit, subset=as.idw.na.omit>50) length(as.idw.as.50) stat.desc(as.idw.as.50)

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as.idw.as.10 10) length(as.idw.as.10) stat.desc(as.idw.as.10)

windows() hist(log10(as.idw.na.omit), breaks=50, col="white", main="Histogram of [As] predicted by IDW", xlab="Log10 [As] (ppb)") abline(v=1.69897, lty=2, col="red") # line of arsenic 50 text(1.85, 150000, "50 ppb", col="red") abline(v=1, lty=2, col="green") text(1.15, 150000, "10 ppb", col="green")

# Write IDW model to ascii file write.asciigrid(as.idw["var1.pred"], "as_idw.asc", na.value=-1)

########### # Compares R-K and O-K (with image) at.as = seq(min(dataset$as), max(dataset$as), sd(dataset$as)/5) as.ok.plt = spplot(as.ok["var1.rk"], col.regions=grey(rev(seq(0,0.97,1/length(at.as)))), at=at.as, main="OK predictions (as)", sp.layout=list("sp.points", pch="+", col="black", dataset)) as.rk.plt = spplot(as.rk["var1.rk"], col.regions=grey(rev(seq(0,0.97,1/length(at.as)))), at=at.as, mai="RK predictions (as)", sp.layout=list("sp.points", pch="+", col="black", dataset)) print(as.ok.plt, split=c(1,1,2,1), more=T) print(as.rk.plt, split=c(2,1,2,1), more=F)

################## # 5. Cross Validation # ################## # CV for IDW # Cross-validations (leave-one-out cross validation method) the result cross.as.idw= krige.cv(as ~ 1, dataset.ov, verbose=F) proj4string (cross.as.idw) = CRS ("+init=epsg:3148") summary(cross.as.idw) stat.desc(cross.as.idw$residual) # export the residual to shp to illustrate in ArgGIS cv.as.idw.residual = cbind(dataset.rawdata$x, dataset.rawdata$y,cross.as.idw$residual) write.dbf(cv.as.idw.residual, file="cv.as.idw.residual.dbf")

# Amount of variation explained by the model 1-var(cross.as.idw$residual, na.rm=T)/var(dataset$as) # IDW model's Root of the mean squared error (RMSE) sqrt(sum(cross.as.idw$residual^2)/length(cross.as.idw$residual)) # Repeated random sub sampling validation # Regression of observed (y) ~ predicted (x) dataset.valid.idw = overlay(as.idw, dataset.valid) #attribute from "as.idw", and coordinates from dataset.idw)

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dataset.valid.idw@data = cbind(dataset.valid@data, dataset.valid.idw@data) regression.cv.idw = lm(dataset.valid.idw$as ~ dataset.valid.idw$var1.pred) summary(regression.cv.idw) plot(dataset.valid.idw$var1.pred, dataset.valid.idw$as) abline(regression.cv.idw, lwd=1.5) # T-test analysis # All of validation dataset t.test(dataset.valid.idw$var1.pred,dataset.valid.idw$as, paired=T, conf.level = 0.95) # validiation dataet